Adaptive Weighted Feature Fusion for Multiscale Atrous Convolution based 1DCNN with Dilated LSTM-aided Fake News Detection using Regional

Abstract

The people present in the world rely on social media for gathering news, and it is mainly because of the development of technologies. The approaches employed in natural language processing are still deficient in judgment factors, and these techniques frequently rely upon political or social circumstances. Numerous low-level communities in the area are curious after experiencing the negative effects caused by the spread of false information in different sectors. Low-resource languages are still distracted, considering the fact that these techniques are extensively employed in the English language. This work aims to provide an analysis of regional language fake news and develop a referral system with advanced techniques to identify fake news in Hindi and Tamil. This proposed model includes (a) Regional Language Text Collection, (b) Text pre-processing, (c) Feature Extraction, (d) Weighted Stacked Feature Fusion, and (e) Fake News Detection. The text data is collected from the standard datasets. The collected text data is pre-processed and given into the feature extraction using Bidirectional Encoder Representations from Transformers (BERT), Transformer networks, and seq2seq network for extracting the three sets of language text features. These extracted feature sets are inserted into the weighted stacked feature fusion model, where the three sets of extracted features are integrated with the optimized weights that are acquired through the Enhanced Osprey Optimization Algorithm (EOOA). Here, weighted fused features are accomplished and passed toward the fake news detection phase. Fake news detection is performed with Multi-scale Atrous Convolution-based One-Dimensional Convolutional Neural Network with Dilated Long Short Term Memory (MACNN-DLSTM). The fake news in the regional language is detected finally. The experimental analysis is carried out by comparing with the conventional algorithms and detection techniques to showcase the efficiency of the developed regional language-based fake news detection model.